Artificial Intelligence and Big Data Strategies for Predictive Maintenance in Industry 4.0: A Systematic Review from 2019 to 2024

Paitan-Ramirez N.; Velveder-Esqueche B.; Sanchez-Espinoza Y.; Ramos-Cosi S. · International Journal of Engineering Trends and Technology (2025) · DOI: 10.14445/22315381/ijett-v73i6p115

Resumen

In recent years, predictive maintenance within Industry 4.0 has acquired considerable relevance, mainly due to its ability to improve operational efficiency and reduce costs. This study aims to systematically analyze the use of Artificial Intelligence and Big Data in predictive maintenance using the STAR methodology. To do so, scientific databases such as Scopus were reviewed, initially obtaining 399 documents, which were filtered until 166 relevant studies. The results show that countries such as India, France, and Germany lead research in this field, while Latin America has a much more limited presence. Regarding thematic areas, Engineering and Computer Science have the highest scientific production, demonstrating the dominant focus on developing these technologies. In summary, incorporating Artificial Intelligence and Big Data in predictive maintenance has established itself as an essential tool for optimizing industrial processes, highlighting the need for multidisciplinary approaches and data-driven strategies to enhance efficiency and sustainability within Industry 4.0.

Instituciones: Universidad de Ciencias y Humanidades